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By Duncan Micklem, Strategy Director at KBC, a Yokogawa Company
The global refining industry is constantly evolving and responding to new crude supplies and sources, more stringent fuel/product specifications, constantly changing demand patterns and more. Increased competition from large, efficient mega refineries is forcing small- to mid-sized refiners to rethink their strategies so they can remain competitive.
Aggressive optimization programs are a very attractive strategy. Marginal refiners can use advanced simulation models and software to secure returns demanded by their stakeholders. These tools and methods are a low-risk approach to value creation. With an appropriate understanding of where and how to optimize assets, these benefits can be sustained.
Historically, the average return on investment (ROI) in refining has been 6%-9%. Refining margins are expected to improve and remain strong in the near-term. However, rapidly evolving technologies, regulations and geopolitics will continue to add more risk on large capital projects. Only a few refiners have the advantage and long-term fundamentals, resources and capabilities to justify and successfully implement any new major investments.
For the rest of the refining industry, achieving or exceeding market returns requires bold action. The two clear options for boosting performance are 1) pursue aggressive asset optimization programs or 2) acquire advantaged assets. Premiums on valuations are making acquisitions of advantaged assets expensive, thus leaving aggressive optimization programs as the most viable option.
The ultimate goal for any optimization program is aligning the supply of premium products as closely as possible to market demand. When product/market demand fluctuates, strategies must be in place to quickly respond and adapt. For refiners, agility is possessing the resilience or operational flexibility to readily adjust production and exploit opportunities. A correctly executed optimization program mitigates any new risks, ensuring the refinery will survive and profit from market volatility (Fig. 1).
Options for modifying, adding or reconfiguring assets to achieve this operational flexibility are limited. Such decisions must consider typically high associated capital intensities and long lead times for such modifications, additions and reconfigurations. Likewise, these plans must address market fluctuations, operational integrity constraints of aging assets and the mindset changes required among operators.
Fig. 1. In today’s competitive business environment, refiners must develop mid- and long-term strategies that afford flexibility and agility when working with constantly changing feedstocks and product demand.
Refiners typically use linear programming (LP) planning models to develop strategies for dealing with processing and market changes, and other opportunities. However, LP models are frequently limited by weak process unit representations and inadequate consideration of physical and logistical constraints. Generally, LP solutions represent recent operating conditions. LP predictive capabilities are outside the present operations are thus suspect, frequently inaccurate and sub-optimal. A more rigorous tool, such as the molecular approach, is needed to find the true optimal economic environment, for both present and future operations.
Optimization plans entail more than crude oil feedstock selection and satisfying finished product demands. The envelope for optimization has grown significantly to include intermediate feedstocks, chemical base stocks and fuel-blending components—as well as energy balance considerations where generated power is a marginal product. In addition, optimization opportunities are present in the logistics side of the business.
As the optimization problem becomes more complex and wider reaching, the tools required for a valid and accurate answer must change. LP technology can no longer be the only tool to solve optimization problems. Value creation entails using a holistic and rigorous approach to maximize the value of all hydrocarbon molecules (not just finished product streams) within their value chains. This requires the organizational “DNA” to act and sustain actions.
The mix of feedstocks available to refiners has never been greater. The growing availability of light tight oil from shale formations, and the number of IOCs and others bringing heavier crudes to market from deepwater and oil sands plays, has broadened the crude basket. In short, crude choices have a massive impact on the array of molecules now possible.
Transportation fuel demand will stabilize globally over the mid- to long-term. In contrast, strong demand growth is predicted for petrochemical feedstocks such as naphtha, LPG and ethane. Accordingly, any refinery optimization program should closely evaluate the potential for molecular, model-based approaches focused on value creation through refinery-petrochemical integration.
There is no lack of competition within the refining industry. Changing crude markets, pipeline reversals and the ability of large and highly efficient US Gulf Coast, Russian and Middle Eastern export refiners to cost-efficiently place refined products into Europe and the US East Coast adds more stress into the market place. Open-mindedness and a sense of urgency are required by refiners regarding the markets being targeted. Such decisions investigate how operating constraints should be addressed and the basis for feedstock (crude) selection.
With International Maritime Organization (IMO) legislation due to be enforced beginning in 2020, refiners with significant high-sulfur fuel oil (HSFO) in their yield slate will be forced to consider changing feedstocks or investing in conversion (sulfur-reduction) technologies. Otherwise, they face possible closure due to lower revenues from the weak HSFO crack spread.
Concurrently, feedstocks are becoming heavier, and those derived from shale have high aromatics, sulfur and concarbon concentrations, and contain sediments with high-metals content. Such changes can constrain the operational flexibility needed by small- to mid-sized marginal refineries to be more competitive. Ultimately, these facilities must find and adopt a new approach.
Marginal refiners need operating strategies to accommodate feedstock and intermediate variability. In addition, these plans must provide refiners with the ability to run economically at lower throughputs to meet market demand, while keeping key units at required capacity. Achieving this strategy requires:
When planning an optimization/competitiveness program, refiners should consider these tools to build their strategy:
As illustrated in Fig. 2, aggressive optimization programs should be operations-focused, take a holistic view and require:
Fig. 2. Strategic directions and planning align assets and workforces with evolving new technologies to optimize not only processing units, but molecules throughout the supply chain.
For success, refinery-wide reactor simulators are needed to accurately model the plant site. They must be calibrated against actual conditions, and the calibration must address and be accurate for all new possible conditions. Kinetic simulations and models, based on physical and chemical laws, are used in place of simple linear regressions of former operations.
If an analysis is to explore operational futures that have never been encountered—such as new feedstocks, product requirements, constraints and different processing conditions—then drawing a straight line from the starting point will never produce a valid answer. In some cases, the end result could be detrimental to the facility. Rigorous simulation will produce accurate answers to support operating/business decisions that result in safe, reliable and profitable operations. They allow:
New advanced simulation/optimization tools have not made the LP obsolete, and it is still the tool of choice for operational planning in many areas. When planning, the proper questions to ask of an LP are: “Does the refinery have the ability to produce the products being demanded; roughly how much shortfall will need to buy in; how much surplus is available for sell; and in what time frame?”
When scheduling, the questions to ask of the LP are: “Given what the refinery is currently producing; what feedstocks should be imported; what are the shortfalls of blendstocks; where are the products stored and how are they sent to the process units; how are the components blended into finished products, stored and exported; and how to dispose surplus blendstocks?”
For both scheduling and planning exercises, linear extrapolation around a known operating point may provide answers to these and similar problem. But, any errors or inaccuracies are “mopped up” by having adequate storage, passing on errors from one round of planning to the next or scheduling onto the next round. The solution is never identified as truly optimum, with feasibility being the over-arching goal.
Rigorous simulation can add significant value to LP planning and scheduling processes and tools. A simulator can generate linear and non-linear models of the plant in areas where it has never been used, such as in a simulated space. These enhanced process unit representations can then be absorbed by the LP to improve planning and scheduling.
A 150,000-bpd refinery—with multiple crudes and varying levels of density, sulfur and volume fractions—was investigating how advanced kinetic reactor models could improve feedstock selection and operations planning, and achieve enhanced strategic investment/capital allocation outcomes.1 The initial task involved validation of the pre-existing process models and calculations for the conversion units, plus detailed tray-to-tray calculations of the distillation columns.
Rigorous models captured the non-linear responses including effects of feed and operational severity on catalyst de-activation, impact of distillation overlap on product specifications, and pump-around or condenser duty constraints.
Thirty optimization cases of increasing levels of complexity were conducted. Initially, crude composition was varied with the crude rate, and unit operating conditions kept constant. Later, crude composition was fixed, and the operating conditions and crude rates were varied.
The refinery constraints were kept simple and similar to the LP. However, they now included the flexibility of using more detailed constraints with the refinery-wide simulation model. The updated constraints were representative of column flooding limits, feed or product pump set points, heater constraints and more. Multiple standard desktop computers ran the software applications. A non-linear algorithm was also used.
The engagement used the following approach:
The engagement clearly demonstrated that after the crude slate was set by the LP, the refinery-wide simulation model could then optimize the operations of various process units. This refinery was able to take advantage of the detailed non-linear reactor models, and of the more differentiated hydrocarbon stream characterization capabilities (Fig. 3). In addition, the refinery was able to carry out more precise operational plans, without having to rely on LP directionality.
Fig. 3. For marginal refiners, refinery-wide optimization efforts take advantage of kinetics-based non-linear models and libraries to improve constrains in LPs, thus pushing operations to greater efficiencies and profitability.
A large integrated refining and petrochemical business was involved in an internal transformation program. The project focused on shifting from refinery to value-chain optimization. A software and consulting company was tasked with developing a broad scope of deliverables as illustrated in Fig. 4. The project involved:
Implementation software tools were selected for the project, and the software and consulting company:
As part of the organization and culture workstream, the structure, skills and capabilities of the present organization were assessed, with a focus on effectively and efficiently executing the defined business processes. A training system was put in place to bridge capability gaps, including training and technology manuals, instructor guides, and a mechanism for training and certification of in-house trainers.
Fig. 4. Successful optimization strategies combine processes technologies, tools, and organizational and culture to set and maintain an improvement program.
Marginal refiners need a new approach to survive the constantly changing refining industry environment. Small- to mid-sized refiners are challenged to compete against larger, more efficient export-oriented refineries. New crude supplies and finished product specifications mandate using a molecular approach to find the optimum planning and scheduling programs.
Marginal refiners must take bold actions to profit from new crudes, while and operating processing units at lower throughputs to meet market demand.
As shown in Fig. 4, OpX involves aligning the organization and culture to implement and stick with the new strategies. Fully utilizing advanced tools (kinetic-based models and software) enables the redefinition of LPs to push process operations into new areas, thus capitalizing on value creation across the supply chain, while still providing flexibility and agility to meet ever-changing demand conditions.
The result is supportive processes to mitigate risks when moving to new operating conditions. Better planning supports improved decision-making, leading to improved profitability, especially for small- and mid-sized refineries. The final piece to the puzzle is partnering with experienced experts to find the proper fit of technologies, tools and organizational culture to implement optimization projects.
Integrate big data to optimize plant data and develop optimal setpoint targets with real-time optimization.
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